We describe Concurrent ALisp, a language that allows the augmentation of reinforcement learning algorithms with prior knowledge about the structure of policies, and show by example how it can be used to write agents that learn to play a subdo-main of the computer game Stratagus.

8 Conclusion
We have outlined an approach to writing programs that play
games like Stratagus using partial programming with concurrent
ALisp, and demonstrated its effectiveness on a subdomain
that would be difficult for conventional reinforcement
learning methods. In the near future, we plan to implement
our improved learning algorithm, and scale up to increasingly
larger subgames within Stratagus.